Top Machine Learning Engineer Jobs in San Francisco Bay Area, CA

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, identity abuse, merchant/marketplace risk, scams, and other adversarial activity. Own end-to-end production lifecycle: data contracts, low-latency inference, batch scoring, feature quality, deployment, monitoring, incident response, rollback, and feedback loops. Develop AI-assisted workflows and reusable decision capabilities while partnering with modelers, product, compliance, and operations.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

The Senior AI ML Engineer will design and implement AI/ML systems for healthcare, manage projects from ideation to release, and mentor engineers. Responsibilities include delivering quality code, driving architectural decisions, and collaborating with cross-functional teams.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Lead the design, development, and deployment of AI software components while partnering with engineers and scientists to deliver innovative AI solutions at Capital One.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Founding ML engineering hire to design and build production ML systems: training and inference pipelines, serving, feature workflows, monitoring, retraining, governance, and operational practices. Partner across engineering, data, product, and applied science to productionize models, implement observability, prevent degradation, and set platform standards and hiring bar for future ML engineers.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Design, deploy, and maintain end-to-end ML risk solutions at scale to detect and prevent fraud, merchant risk, and credit loss. Partner with cross-functional teams, lead technical decisions, build ML tooling, monitor models in production, and investigate emerging abuse patterns to improve detection and decisioning.

Build and scale Gusto's ML/AI platform: design MLOps solutions, automated pipelines, model development frameworks, deployment, monitoring, CI/CD, and observability; collaborate with ML engineers and apply AI tools and best practices to production ML systems.

Lead design and productionalize ML algorithms and pipelines to detect account-sharing fraud in real time. Build scalable preprocessing, training, deployment, monitoring, and A/B testing solutions with cross-functional teams to optimize detection while minimizing user disruption.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Seeking a Perception ML Engineer to develop and deploy advanced perception models for robotics, focusing on manipulation and navigation in dynamic environments.

The Machine Learning Engineer role involves optimizing models for performance, utilizing CUDA, GPU scheduling, and collaborating on ML systems design.

You'll lead the development of AI-driven systems for a dating platform, focusing on personalized matching algorithms and voice interactions. Responsibilities include designing ML pipelines, deploying models, and conducting experiments to improve user experience.

Develop and deploy manufacturing intelligence systems and machine learning models to enhance operational efficiency in production environments, collaborating across teams to achieve impactful solutions.

As an AI/ML Engineer, you will design and deploy models for real-world applications, optimize data pipelines, and engage with global leaders to enhance AI technology's impact across sectors like healthcare and government.

As a Machine Learning Engineer at Onyx, you will enhance agent and knowledge retrieval capabilities by implementing LLM-based graphs and advanced NLP algorithms, while collaborating with the team to improve user experience and product direction.

Lead the design of advanced AI systems for healthcare, develop evaluation frameworks, and drive LLM development while shaping AI strategy in a collaborative environment.

The role involves training and deploying ASR models, improving pronunciation feedback, measuring performance, and expanding ASR systems for language learning.

Build and automate large-scale ML data pipelines and tooling for autonomous trucking. Improve dataset curation, deduplication, labeling, evaluation, and metrics infrastructure. Collaborate with robotics, autonomy, and infra teams to support continuous learning, deployment, and scalable AI infrastructure for Kodiak's fleet.

The role involves developing and maintaining behavioral models for road users to enhance autonomous vehicle decision-making, collaborating with other teams and optimizing outputs for safety and effectiveness.

Take end-to-end ownership of ML problems from framing and experimentation through deployment and support in customer environments. Build, deploy, and maintain production ML models that inform engineering decisions, and communicate technical tradeoffs to cross-functional and non-technical stakeholders.

Optimize and deploy multi-modal models (transformers, diffusion, VLMs) to run on-device across mobile and desktop. Own export, quantization, kernel tuning, runtime integration, and profiling to meet strict latency, memory, and power budgets. Build packaging, benchmarking, and telemetry; partner with research to productionize models and mentor junior engineers.